Reference : Application of Shape Analysis on 3D Images - MRI of Renal Tumors
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Multidisciplinary, general & others
Application of Shape Analysis on 3D Images - MRI of Renal Tumors
Schiltz, Jang mailto [University of Luxembourg > Faculty of Law, Economics and Finance (FDEF) > Luxembourg School of Finance (LSF) >]
Giebel, Stefan []
Graf, Norbert [University hospital of Homburg > Department of Pediatric Oncology and Hematology]
Nourkami, Nasenien [University Hospital of Homburg > Department of Pediatric Oncology and Hematology]
Leuschner, Ivo [Schleswig-Holstein University Campus Kiel > Department of Paleopathology]
Schenk, Jens-Peter [University hospital of Heidelberg > Division of Pediatric Radiology]
Journal of the Iranian Statistical Society
[en] Neural networks ; Radiology ; Statistical Shape Analysis
[en] The image recognotion and the classification of objects
according to the images are more in focus of interests, especially in
medicine. A mathematical procedure allows us, not only to evaluate
the amount of data per se, but also ensures that each image is processed
similarly. Here in this study, we propose the power of shape
analysis, in conjunction with neural networks for reducing white noise
instead of searching an optimal metric, to support the user in his evaluation
of MRI of renal tumors. Therapy of renal tumors in childhood
bases on therapy optimizing SIOP(Society of Pediatric Oncology and
Hematology)-study protocols in Europe. The most frequent tumor is
the nephroblastoma. Other tumor entities in the retroperitoneum are
clear cell sarcoma, renal cell carcinoma and extrarenal tumors, especially
neuroblastoma. Radiological diagnosis is produced with the help of cross sectional imaging methods (computertomography CT or Magnetic
Resonance Images MRI). Our research is the first mathematical
approach on MRI of retroperitoneal tumors (n=108). We use MRI in 3
planes and evaluate their potential to differentiate other types of tumor
by Statistical Shape Analysis. Statistical shape Analysis is a methology
for analyzing shapes in the presence of randomness. It allows to study
two- or more dimensional objects, summarized according to key points
called landmarks, with a possible correction of size and position of the
object. To get the shape of an object without information about position
and size, centralisation and standardisation procedures are used in
some metric space. This approach provides an objective methodology
for classification whereas even today in many applications the decision
for classifying according to the appearance seems at most intuitive.
We determine the key points or three dimensional landmarks of
retroperitoneal tumors in childhood by using the edges of the platonic
body (C60) and test the difference between the groups (nephroblastoma
versus non-nephroblastoma).

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